Abstract
Companies’ operations have become over-dependent on their supporting enterprise software applications. This situation has placed a heavy burden onto software maintenance teams who are expected to keep these applications up and running optimally in varying execution conditions. However, this high human intervention drives up the overall costs of software ownership. In addition, the current dynamic nature of enterprise applications constitutes challenges with respect to their architectural design and development, and the guarantee of the agreed quality requirements at runtime. Efficiently and effectively achieving the adaptation of enterprise applications requires an autonomic solution. In this paper we present SHIFT, a framework that provides (i) facilities and mechanisms for managing self-adaptive enterprise applications through the use of an autonomic infrastructure, and (ii) automated derivation of self-adaptive enterprise applications and their respective monitoring infrastructure. Along with the framework, our work leads us to propose a reference specification and architectural design for implementing self-adaptation autonomic infrastructures. We developed a reference implementation of SHIFT; our contribution includes the development of monitoring infrastructures, and dynamic adaptation planning and automated derivation strategies.
Highlights
Enterprise Applications (EAs) have become key assets for all modern organizations, holding huge amounts of data and providing concurrent user access to such information as well as processing services for it
We developed a reference implementation of SHIFT; our contribution includes the development of monitoring infrastructures, and dynamic adaptation planning and automated derivation strategies
We have identified a lack of a detailed, standard reference specification and architectural design for building autonomic infrastructures
Summary
Enterprise Applications (EAs) have become key assets for all modern organizations, holding huge amounts of data and providing concurrent user access to such information as well as processing services for it. They propose a middleware for monitoring cloud services defined around a monitoring process that uses models at runtime capturing low- and high-level non-functional requirements from Service Level Agreements (SLAs) Their middleware only provides a partial implementation of an autonomic infrastructure, of the monitor and analyzer elements. We proposed independent approaches and implementations in the contexts of the engineering of highly dynamic adaptive software systems with the DYNAMICO reference model [3] model-based product line engineering with the FIESTA approach [24]–[26]; automated reasoning for derivation of product lines [27]; and the recent (unpublished) contributions regarding quality variations in the automated derivation process of product lines [26]. The SHIFT framework is motivated by the required integration of all these efforts as part of the SHIFT research project in a move to approach automation and quality awareness along the life cycle of enterprise applications
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